
import numpy as np
import pandas as pd
import os,sys
from pathlib import Path
import matplotlib.pyplot as plt

cwd = os.getcwd() #grabs current working directory

testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'EnergyAnalysis')
inputfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP')
baselinesfolder = str(Path().resolve().parent.parent /'PV_ICE' / 'baselines')
supportMatfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'SupportingMaterial')
altBaselinesfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'Energy_CellModuleTechCompare')

if not os.path.exists(testfolder):
    os.makedirs(testfolder)


from platform import python_version 
print(python_version())

3.8.8


#https://www.learnui.design/tools/data-color-picker.html#palette
#color pallette - modify here for all graphs below
colorpalette=['#000000', #PV ICE baseline
              '#003f5c','#7a5195','#ef5675','#ffa600', #extreme cases (4)
              '#0e5c5c','#107575','#118f8f','#10aaaa','#0ec5c6','#09e2e2','#00ffff'] #realistic cases (7)
            #tealish ['#0e5c5c','#107575','#118f8f','#10aaaa','#0ec5c6','#09e2e2','#00ffff']
            #blues ['#004c6d','#00678a','#0083a6','#00a1c1','#00c0d8','#00dfed','#00ffff']
colormats = ['#00bfbf','#ff7f0e','#1f77be','#2ca02c','#d62728','#9467BD','#8C564B'] #colors for material plots       

import matplotlib as mpl #import matplotlib
from cycler import cycler #import cycler
mpl.rcParams['axes.prop_cycle'] = cycler(color=colorpalette) #reset the default color palette of mpl

plt.rcParams.update({'font.size': 14})
plt.rcParams['figure.figsize'] = (8, 6)

scennames_labels = ['PV_ICE','50-year','High Eff','Idealized\nPerovskite\nSi-Tandem','Lightweight',
                    'PERC','SHJ','TOPCon','IRENA\nreg. loss','Recycled\nPERC','long-lived\nPERC','Hopeful\nPerovskite'] #


#creating scenarios for identical power of multiple technologies, moved to below due to reordering
scennames_ex = ['PV_ICE', 'ex_PERC_50', 'ex_High_eff', 'ex_Perovskite', 'ex_Lightweight'] #extreme cases
scennames_r = ['PV_ICE','r_PERC', 'r_SHJ', 'r_TOPCon', 'r_IRENA', 'r_RecycledPERC', 'r_50PERC', 'r_Perovskite'] #realistic cases

MATERIALS = ['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet']
moduleFile_m = os.path.join(baselinesfolder, 'baseline_modules_mass_US.csv')
moduleFile_e = os.path.join(baselinesfolder, 'baseline_modules_energy.csv')


#load in a baseline and materials for modification
import PV_ICE

sim1 = PV_ICE.Simulation(name='sim1', path=testfolder)
for scen in scennames_ex:
    sim1.createScenario(name=scen, massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
    for mat in range (0, len(MATERIALS)):
        matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
        matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
        sim1.scenario[scen].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)

path = C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\TEMP\EnergyAnalysis


PV_ICE.__version__

'v0.2.0+412.gc3c132e.dirty'


sim1.scenario.keys()

dict_keys(['PV_ICE', 'ex_PERC_50', 'ex_High_eff', 'ex_Perovskite', 'ex_Lightweight'])


celltech_modeff = pd.read_csv(os.path.join(supportMatfolder, 'output-celltech-modeffimprovements.csv'),index_col=0) #pull in module eff
celltech_aguse = pd.read_csv(os.path.join(supportMatfolder, 'output-celltech-Agusageimprovements.csv'),index_col=0) #pull in Ag use


celltech_modeff.loc[2020]

PERC      22.8
SHJ       23.5
TOPCon    23.2
Name: 2020, dtype: float64


#glass-glass package mass per area calculation
#ITRPV 2022 Figs 36 and 38, we are assuming that the front and back glass heave equal thickness of 2.5mm
density_glass = 2500*1000 # g/m^3 
glassperm2 = (2.5/1000)* 2 * density_glass
print('The mass per module area of glass is '+str(glassperm2)+' g/m^2 for all modules with a glass-glass package')

The mass per module area of glass is 12500.0 g/m^2 for all modules with a glass-glass package


timeshift = 2022-1995


#module efficiency modify for PERC
sim1.modifyScenario('ex_PERC_50', 'mod_eff', celltech_modeff.loc[2022:,'PERC'], start_year=2022) #changing module eff


#silver modify for PERC
sim1.scenario['ex_PERC_50'].modifyMaterials('silver', 'mat_massperm2', celltech_aguse.loc[2022:,'PERC'], start_year=2022)
#old way
#sim1.scenario['PERC_50'].material['silver'].matdataIn_m.loc[timeshift:,'mat_massperm2'] = celltech_aguse.loc[2022:,'PERC'].values


#modify package to glass glass
sim1.scenario['ex_PERC_50'].modifyMaterials('glass', 'mat_massperm2', glassperm2, start_year=2022) #


#Lifetime and Degradation
#values taken from lifetime vs recycling paper
#degradation rate:
sim1.modifyScenario('ex_PERC_50', 'mod_degradation', 0.445, start_year=2022) #annual power degradation to reach 80% at 50 yrs
#T50
sim1.modifyScenario('ex_PERC_50', 'mod_reliability_t50', 56.07, start_year=2022)
#t90
sim1.modifyScenario('ex_PERC_50', 'mod_reliability_t90', 59.15, start_year=2022) 
#Mod Project Lifetime
sim1.modifyScenario('ex_PERC_50', 'mod_lifetime', 50, start_year=2022) #project lifetime of 50 years


#Merchant Tail set high
sim1.modifyScenario('ex_PERC_50', 'mod_MerchantTail', 100, start_year=2022) #all installations stay for merchant tail
#Change recycling?


idx_temp = pd.RangeIndex(start=2022,stop=2051,step=1) #create the index
df_higheff = pd.DataFrame(index=idx_temp, columns=['mod_eff_p'], dtype=float)
df_higheff.loc[2022] = 22.0
df_higheff.loc[2030] = 25.0
df_higheff.loc[2050] = 30.0
df_higheff.interpolate(inplace=True)
#module efficiency modify for PERC
sim1.modifyScenario('ex_High_eff', 'mod_eff', df_higheff.loc[2022:,], start_year=2022) #changing module eff


#Lifetime and Degradation
#values taken from lifetime vs recycling paper
#degradation rate:
sim1.modifyScenario('ex_High_eff', 'mod_degradation', 0.7, start_year=2022) #standard current degrdation
#T50
sim1.modifyScenario('ex_High_eff', 'mod_reliability_t50', 25, start_year=2022)
#t90
sim1.modifyScenario('ex_High_eff', 'mod_reliability_t90', 31, start_year=2022) 
#Mod Project Lifetime
sim1.modifyScenario('ex_High_eff', 'mod_lifetime', 25, start_year=2022) #project lifetime of 25 years


#2022 module eff = 17.9% #https://www.nrel.gov/pv/assets/pdfs/champion-module-efficiencies-rev220401b.pdf
#2050 module eff = 27.3% # Sofia et al 2019 4T future
idx_perovskite_eff = pd.RangeIndex(start=2022,stop=2051,step=1) #create the index
df_perovskite_eff = pd.DataFrame(index=idx_perovskite_eff, columns=['mod_eff_p'], dtype=float)
df_perovskite_eff.loc[2022] = 17.9
df_perovskite_eff.loc[2030] = 27.3
df_perovskite_eff.loc[2050] = 27.3
df_perovskite_eff.interpolate(inplace=True)


#module efficiency modify for PERC
sim1.modifyScenario('ex_Perovskite', 'mod_eff', df_perovskite_eff['mod_eff_p'], start_year=2022) #changing module eff


#modify package to glass glass
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_massperm2', glassperm2, start_year=2022)


#Lifetime and Degradation
#values taken from lifetime vs recycling paper
#degradation rate:
sim1.modifyScenario('ex_Perovskite', 'mod_degradation', 1.47, start_year=2022) #annual power degradation
#Mod Project Lifetime
sim1.modifyScenario('ex_Perovskite', 'mod_lifetime', 15, start_year=2022) #project lifetime of 15 years
#T50
sim1.modifyScenario('ex_Perovskite', 'mod_reliability_t50', 14, start_year=2022)
#t90
sim1.modifyScenario('ex_Perovskite', 'mod_reliability_t90', 20, start_year=2022)


#As Circular as possible
#100% collection rate
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_collection_eff', 100.0, start_year=2022) #100% collection
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pg1_landfill', 0.0, start_year=2022) #100% collection
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pb1_landfill', 0.0, start_year=2022) #100% collection

# remanufacturing
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pg3_reMFG', 100.0, start_year=2022) #all modules attempt remfg
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_sp_reMFG_recycle', 100.0, start_year=2022) # recycle if can't remfg
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pb3_reMFG', 100.0, start_year=2022) # remfg bad mods too
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_reMFG_yield', 98.0, start_year=2022) # REMFG YIELD 98%

#set all other paths to 0
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pg0_resell', 0.0, start_year=2022) # 
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pg1_landfill', 0.0, start_year=2022) # 
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pg2_stored', 0.0, start_year=2022) #
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pg4_recycled', 0.0, start_year=2022) # 
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pb1_landfill', 0.0, start_year=2022) # 
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pb2_stored', 0.0, start_year=2022) # 
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pb4_recycled', 0.0, start_year=2022) # 
sim1.modifyScenario('ex_Perovskite', 'mod_Repair', 0.0, start_year=2022) #
sim1.modifyScenario('ex_Perovskite', 'mod_MerchantTail', 0.0, start_year=2022) #


#Material Remanufacture
#Glass
#mfg scrap
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_MFG_scrap_Recycled', 100.0, start_year=2022) #send mfg scrap to recycle
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_MFG_scrap_Recycling_eff', 98.0, start_year=2022) #99% yield
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_MFG_scrap_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
#eol
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_PG3_ReMFG_target', 100.0, start_year=2022) #send all to remfg
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_PG4_Recycling_target', 100.0, start_year=2022) #send none to recycle
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_ReMFG_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild


#silicon ReMFG 
#mfg scrap
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled', 100.0, start_year=2022) #send mfg scrap to recycle
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycling_eff', 98.0, start_year=2022) #98% yield
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
#eol
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_PG3_ReMFG_target', 100.0, start_year=2022) #send to remfg
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_ReMFG_yield', 98.0, start_year=2022) #98% yeild
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_PG4_Recycling_target', 0.0, start_year=2022) #send to remfg
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop


#aluminium_frames recycle
#mfg scrap
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled', 100.0, start_year=2022) #send mfg scrap to recycle
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycling_eff', 98.0, start_year=2022) #98% yield
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
#eol
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_PG4_Recycling_target', 100.0, start_year=2022) #send to recycle
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_ReMFG_yield', 0.0, start_year=2022) #0% yeild


#module efficiency modify for PERC
sim1.modifyScenario('ex_Lightweight', 'mod_eff', celltech_modeff.loc[2022:,'PERC'], start_year=2022) #changing module eff


#2mm glass calculation
lightweightglass = (2.0/1000)* 1 * density_glass

#50% encapsulant
light_encap = pd.Series(sim1.scenario['PV_ICE'].material['encapsulant'].matdataIn_m.loc[timeshift:,'mat_massperm2']*0.5)

#10% silicon
light_Si = pd.Series(sim1.scenario['PV_ICE'].material['silicon'].matdataIn_m.loc[timeshift:,'mat_massperm2']*0.1)

#modify BOM
sim1.scenario['ex_Lightweight'].modifyMaterials('glass', 'mat_massperm2', lightweightglass, start_year=2022) #glass
sim1.scenario['ex_Lightweight'].modifyMaterials('aluminium_frames', 'mat_massperm2', 0.0, start_year=2022) #frameless
sim1.scenario['ex_Lightweight'].modifyMaterials('encapsulant', 'mat_massperm2', light_encap, start_year=2022) #50% encapsulant
sim1.scenario['ex_Lightweight'].modifyMaterials('silicon', 'mat_massperm2', light_Si, start_year=2022) #10% silicon


#Lifetime and Degradation
#values taken from lifetime vs recycling paper
#degradation rate:
sim1.modifyScenario('ex_Lightweight', 'mod_degradation', 0.7, start_year=2022) #annual power degradation
#Mod Project Lifetime
sim1.modifyScenario('ex_Lightweight', 'mod_lifetime', 25, start_year=2022) #project lifetime of 15 years
#T50
sim1.modifyScenario('ex_Lightweight', 'mod_reliability_t50', 25, start_year=2022) #this will get changed with IRENA alpha/b
#t90
sim1.modifyScenario('ex_Lightweight', 'mod_reliability_t90', 31, start_year=2022) #this will get changed with IRENA alpha/b


len(sim1.scenario['ex_Lightweight'].material['silicon'].matdataIn_m)

56


altBaselinesfolder

'C:\\Users\\hmirletz\\Documents\\GitHub\\PV_ICE\\PV_ICE\\baselines\\Energy_CellModuleTechCompare'


#modified module
moduleinput_m_r_PERC = os.path.join(altBaselinesfolder, 'mod_r_PERC.csv')
sim1.createScenario(name='r_PERC', massmodulefile=moduleinput_m_r_PERC, energymodulefile=moduleFile_e)


#module efficiency modify for PERC
sim1.modifyScenario('r_PERC', 'mod_eff', celltech_modeff.loc[2022:,'PERC'], start_year=2022) #changing module eff
#sim1.scenario['SHJ'].dataIn_m.loc[timeshift:,'mod_eff'] = celltech_modeff.loc[2022:,'SHJ'].values


#modified materials
#glass
matinput_glass_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_glass_improvedRecycle.csv')
matbaseline_e_glass = os.path.join(baselinesfolder,'baseline_material_energy_glass.csv')
sim1.scenario['r_PERC'].addMaterial('glass', massmatfile=matinput_glass_m_improvedRecycle, energymatfile=matbaseline_e_glass)
#silicon
matinput_silicon_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silicon_improvedRecycle.csv')
matbaseline_e_si = os.path.join(baselinesfolder,'baseline_material_energy_silicon.csv')
sim1.scenario['r_PERC'].addMaterial('silicon', massmatfile=matinput_silicon_m_improvedRecycle, energymatfile=matbaseline_e_si)
#silver
matinput_silver_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silver_improvedRecycle.csv')
matbaseline_e_ag = os.path.join(baselinesfolder,'baseline_material_energy_silver.csv')
sim1.scenario['r_PERC'].addMaterial('silver', massmatfile=matinput_silver_m_improvedRecycle, energymatfile=matbaseline_e_ag)
#aluminium frames
matinput_Al_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_aluminium_frames_improvedRecycle.csv')
matbaseline_e_al = os.path.join(baselinesfolder,'baseline_material_energy_aluminium_frames.csv')
sim1.scenario['r_PERC'].addMaterial('aluminium_frames', massmatfile=matinput_Al_m_improvedRecycle, energymatfile=matbaseline_e_al)

#standard materials
baselineMats=['copper', 'encapsulant', 'backsheet']
for mats in range (0, len(baselineMats)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+baselineMats[mats]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+baselineMats[mats]+'.csv')
    sim1.scenario['r_PERC'].addMaterial(baselineMats[mats], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#modify silver usage for PERC
sim1.scenario['r_PERC'].modifyMaterials('silver', 'mat_massperm2', celltech_aguse.loc[2022:,'PERC'], start_year=2022)


#modify package to glass glass
sim1.scenario['r_PERC'].modifyMaterials('glass', 'mat_massperm2', glassperm2, start_year=2022)


#modified module
moduleinput_m_r_SHJ = os.path.join(altBaselinesfolder, 'mod_r_SHJ.csv')
sim1.createScenario(name='r_SHJ', massmodulefile=moduleinput_m_r_SHJ, energymodulefile=moduleFile_e)


#module efficiency modify for SHJ
sim1.modifyScenario('r_SHJ', 'mod_eff', celltech_modeff.loc[2022:,'SHJ'], start_year=2022) #changing module eff
#sim1.scenario['SHJ'].dataIn_m.loc[timeshift:,'mod_eff'] = celltech_modeff.loc[2022:,'SHJ'].values


#load modified materials
#modified materials
#glass
matinput_glass_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_glass_improvedRecycle.csv')
matbaseline_e_glass = os.path.join(baselinesfolder,'baseline_material_energy_glass.csv')
sim1.scenario['r_SHJ'].addMaterial('glass', massmatfile=matinput_glass_m_improvedRecycle, energymatfile=matbaseline_e_glass)
#silicon
matinput_silicon_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silicon_improvedRecycle.csv')
matbaseline_e_si = os.path.join(baselinesfolder,'baseline_material_energy_silicon.csv')
sim1.scenario['r_SHJ'].addMaterial('silicon', massmatfile=matinput_silicon_m_improvedRecycle, energymatfile=matbaseline_e_si)
#silver
matinput_silver_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silver_improvedRecycle.csv')
matbaseline_e_ag = os.path.join(baselinesfolder,'baseline_material_energy_silver.csv')
sim1.scenario['r_SHJ'].addMaterial('silver', massmatfile=matinput_silver_m_improvedRecycle, energymatfile=matbaseline_e_ag)
#aluminium frames
matinput_Al_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_aluminium_frames_improvedRecycle.csv')
matbaseline_e_al = os.path.join(baselinesfolder,'baseline_material_energy_aluminium_frames.csv')
sim1.scenario['r_SHJ'].addMaterial('aluminium_frames', massmatfile=matinput_Al_m_improvedRecycle, energymatfile=matbaseline_e_al)

#standard materials
baselineMats=['copper', 'encapsulant', 'backsheet']
for mats in range (0, len(baselineMats)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+baselineMats[mats]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+baselineMats[mats]+'.csv')
    sim1.scenario['r_SHJ'].addMaterial(baselineMats[mats], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#modify silver usage for SHJ
sim1.scenario['r_SHJ'].modifyMaterials('silver', 'mat_massperm2', celltech_aguse.loc[2022:,'SHJ'], start_year=2022)


#modify package to glass glass
sim1.scenario['r_SHJ'].modifyMaterials('glass', 'mat_massperm2', glassperm2, start_year=2022)


#Merchant Tail set high
#sim1.modifyScenario('SHJ', 'mod_MerchantTail', 50, start_year=2022) #50% stay for merchant tail, possibly change to DYNAMIC


plt.plot(sim1.scenario['r_SHJ'].material['silicon'].matdataIn_m.iloc[:,0],sim1.scenario['r_SHJ'].material['silicon'].matdataIn_m.iloc[:,3:])
plt.legend(sim1.scenario['PV_ICE'].material['silicon'].matdataIn_m.iloc[:,3:].columns, 
           bbox_to_anchor=(0, -0.1), loc='upper left', ncol=1)
plt.title('Silicon Management Evolution')

Text(0.5, 1.0, 'Silicon Management Evolution')


plt.plot(sim1.scenario['r_SHJ'].material['glass'].matdataIn_m.iloc[:,0],sim1.scenario['r_SHJ'].material['glass'].matdataIn_m.iloc[:,3:])
plt.legend(sim1.scenario['PV_ICE'].material['glass'].matdataIn_m.iloc[:,3:].columns, 
           bbox_to_anchor=(0, -0.1), loc='upper left', ncol=1)
plt.title('Glass Management Evolution')

Text(0.5, 1.0, 'Glass Management Evolution')


plt.plot(sim1.scenario['r_SHJ'].material['aluminium_frames'].matdataIn_m.iloc[:,0],sim1.scenario['r_SHJ'].material['aluminium_frames'].matdataIn_m.iloc[:,3:])
plt.legend(sim1.scenario['PV_ICE'].material['aluminium_frames'].matdataIn_m.iloc[:,3:].columns, 
           bbox_to_anchor=(0, -0.1), loc='upper left', ncol=1)
plt.title('Glass Management Evolution')

Text(0.5, 1.0, 'Glass Management Evolution')


plt.plot(sim1.scenario['r_SHJ'].material['silver'].matdataIn_m.iloc[:,0],sim1.scenario['r_SHJ'].material['silver'].matdataIn_m.iloc[:,3:])
plt.legend(sim1.scenario['PV_ICE'].material['aluminium_frames'].matdataIn_m.iloc[:,3:].columns, 
           bbox_to_anchor=(0, -0.1), loc='upper left', ncol=1)
plt.title('Glass Management Evolution')

Text(0.5, 1.0, 'Glass Management Evolution')


#modified module
moduleinput_m_r_TOPCon = os.path.join(altBaselinesfolder, 'mod_r_TOPCon.csv')
sim1.createScenario(name='r_TOPCon', massmodulefile=moduleinput_m_r_TOPCon, energymodulefile=moduleFile_e)


#module efficiency modify
sim1.modifyScenario('r_TOPCon', 'mod_eff', celltech_modeff.loc[2022:,'TOPCon'], start_year=2022) #changing module eff
#sim1.scenario['SHJ'].dataIn_m.loc[timeshift:,'mod_eff'] = celltech_modeff.loc[2022:,'SHJ'].values


#modified materials
#glass
matinput_glass_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_glass_improvedRecycle.csv')
matbaseline_e_glass = os.path.join(baselinesfolder,'baseline_material_energy_glass.csv')
sim1.scenario['r_TOPCon'].addMaterial('glass', massmatfile=matinput_glass_m_improvedRecycle, energymatfile=matbaseline_e_glass)
#silicon
matinput_silicon_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silicon_improvedRecycle.csv')
matbaseline_e_si = os.path.join(baselinesfolder,'baseline_material_energy_silicon.csv')
sim1.scenario['r_TOPCon'].addMaterial('silicon', massmatfile=matinput_silicon_m_improvedRecycle, energymatfile=matbaseline_e_si)
#silver
matinput_silver_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silver_improvedRecycle.csv')
matbaseline_e_ag = os.path.join(baselinesfolder,'baseline_material_energy_silver.csv')
sim1.scenario['r_TOPCon'].addMaterial('silver', massmatfile=matinput_silver_m_improvedRecycle, energymatfile=matbaseline_e_ag)
#aluminium frames
matinput_Al_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_aluminium_frames_improvedRecycle.csv')
matbaseline_e_al = os.path.join(baselinesfolder,'baseline_material_energy_aluminium_frames.csv')
sim1.scenario['r_TOPCon'].addMaterial('aluminium_frames', massmatfile=matinput_Al_m_improvedRecycle, energymatfile=matbaseline_e_al)

#standard materials
baselineMats=['copper', 'encapsulant', 'backsheet']
for mats in range (0, len(baselineMats)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+baselineMats[mats]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+baselineMats[mats]+'.csv')
    sim1.scenario['r_TOPCon'].addMaterial(baselineMats[mats], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#modify silver usage for PERC
sim1.scenario['r_TOPCon'].modifyMaterials('silver', 'mat_massperm2', celltech_aguse.loc[2022:,'TOPCon'], start_year=2022)


#modify package to glass glass
sim1.scenario['r_TOPCon'].modifyMaterials('glass', 'mat_massperm2', glassperm2, start_year=2022)


#modified module
moduleinput_m_r_IRENA = os.path.join(altBaselinesfolder, 'mod_r_IRENAregloss.csv')
sim1.createScenario(name='r_IRENA', massmodulefile=moduleinput_m_r_IRENA, energymodulefile=moduleFile_e)


#modified materials
#glass
matinput_glass_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_glass_improvedRecycle.csv')
matbaseline_e_glass = os.path.join(baselinesfolder,'baseline_material_energy_glass.csv')
sim1.scenario['r_IRENA'].addMaterial('glass', massmatfile=matinput_glass_m_improvedRecycle, energymatfile=matbaseline_e_glass)
#silicon
matinput_silicon_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silicon_improvedRecycle.csv')
matbaseline_e_si = os.path.join(baselinesfolder,'baseline_material_energy_silicon.csv')
sim1.scenario['r_IRENA'].addMaterial('silicon', massmatfile=matinput_silicon_m_improvedRecycle, energymatfile=matbaseline_e_si)
#silver
matinput_silver_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silver_improvedRecycle.csv')
matbaseline_e_ag = os.path.join(baselinesfolder,'baseline_material_energy_silver.csv')
sim1.scenario['r_IRENA'].addMaterial('silver', massmatfile=matinput_silver_m_improvedRecycle, energymatfile=matbaseline_e_ag)
#aluminium frames
matinput_Al_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_aluminium_frames_improvedRecycle.csv')
matbaseline_e_al = os.path.join(baselinesfolder,'baseline_material_energy_aluminium_frames.csv')
sim1.scenario['r_IRENA'].addMaterial('aluminium_frames', massmatfile=matinput_Al_m_improvedRecycle, energymatfile=matbaseline_e_al)

#standard materials
baselineMats=['copper', 'encapsulant', 'backsheet']
for mats in range (0, len(baselineMats)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+baselineMats[mats]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+baselineMats[mats]+'.csv')
    sim1.scenario['r_IRENA'].addMaterial(baselineMats[mats], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#modified module
moduleinput_m_r_RecyclePERC = os.path.join(altBaselinesfolder, 'mod_r_PERCrecycleSi.csv')
sim1.createScenario(name='r_RecycledPERC', massmodulefile=moduleinput_m_r_RecyclePERC, energymodulefile=moduleFile_e) 

for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['r_RecycledPERC'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#glass recycling
#eol
sim1.scenario['r_RecycledPERC'].modifyMaterials('glass', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['r_RecycledPERC'].modifyMaterials('glass', 'mat_PG4_Recycling_target', 100.0, start_year=2022) #send to recycle
#sim1.scenario['r_RecycledPERC'].modifyMaterials('glass', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['r_RecycledPERC'].modifyMaterials('glass', 'mat_EOL_Recycled_into_HQ', 0.0, start_year=2022) #all HQ
sim1.scenario['r_RecycledPERC'].modifyMaterials('glass', 'mat_EOL_RecycledHQ_Reused4MFG', 0.0, start_year=2022) #closed-loop

#silicon recycled
#mfg scrap
sim1.scenario['r_RecycledPERC'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled', 100.0, start_year=2022) #send mfg scrap to recycle
sim1.scenario['r_RecycledPERC'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycling_eff', 98.0, start_year=2022) #98% yield
sim1.scenario['r_RecycledPERC'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['r_RecycledPERC'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
#eol
sim1.scenario['r_RecycledPERC'].modifyMaterials('silicon', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['r_RecycledPERC'].modifyMaterials('silicon', 'mat_PG4_Recycling_target', 100.0, start_year=2022) #send to recycle
sim1.scenario['r_RecycledPERC'].modifyMaterials('silicon', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['r_RecycledPERC'].modifyMaterials('silicon', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['r_RecycledPERC'].modifyMaterials('silicon', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop

#aluminium_frames recycle
#mfg scrap
sim1.scenario['r_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled', 100.0, start_year=2022) #send mfg scrap to recycle
sim1.scenario['r_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycling_eff', 98.0, start_year=2022) #98% yield
sim1.scenario['r_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['r_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
#eol
sim1.scenario['r_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['r_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_PG4_Recycling_target', 100.0, start_year=2022) #send to recycle
sim1.scenario['r_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['r_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['r_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop

#silver recycling improvement
#create improvement series
idx_temp = pd.RangeIndex(start=2022,stop=2051,step=1) #create the index
df_silver_recycleimprove = pd.DataFrame(index=idx_temp, columns=['mat_PG4_Recycling_target'], dtype=float)
df_silver_recycleimprove.loc[2022] = 0
df_silver_recycleimprove.loc[2030] = 40
df_silver_recycleimprove.loc[2050] = 100
df_silver_recycleimprove.interpolate(inplace=True)

#eol
sim1.scenario['r_RecycledPERC'].modifyMaterials('silver', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['r_RecycledPERC'].modifyMaterials('silver', 'mat_PG4_Recycling_target',
                                                df_silver_recycleimprove.loc[2022:,'mat_PG4_Recycling_target'], start_year=2022) 
sim1.scenario['r_RecycledPERC'].modifyMaterials('silver', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['r_RecycledPERC'].modifyMaterials('silver', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['r_RecycledPERC'].modifyMaterials('silver', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop


#the cool feature of this module was that the wafer was directly put into the Cz process
#therefore, we need to modify the recycling energy to reflect this
#this energy profile is output by the (baseline development) Energy Silicon Manufacturing journal.
altHQRecycle_e = pd.read_csv(os.path.join(supportMatfolder, 'output_energy_silicon_eol_recycleHQ_ALT.csv'), index_col=0)
altHQRecycle_e


#modify silicon recycling energy
sim1.scenario['r_RecycledPERC'].modifyMaterialEnergy('silicon', 'e_mat_Recycled_HQ', altHQRecycle_e.loc[2022:,'E_MFG_kWhpkg'], start_year=2022)
sim1.scenario['r_RecycledPERC'].modifyMaterialEnergy('silicon', 'e_mat_Recycled_HQ_fuelfraction', altHQRecycle_e.loc[2022:,'E_mfgFuelFraction'], start_year=2022)


sim1.scenario['r_RecycledPERC'].material['silicon'].matdataIn_e.iloc[25:30,]


#modified module
moduleinput_m_r_50PERC = os.path.join(altBaselinesfolder, 'mod_r_50PERC.csv')
sim1.createScenario(name='r_50PERC', massmodulefile=moduleinput_m_r_50PERC, energymodulefile=moduleFile_e) 

#load standard materials
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['r_50PERC'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#glass recycling
#create improvement series
idx_temp = pd.RangeIndex(start=2022,stop=2051,step=1) #create the index
df_glass_target = pd.DataFrame(index=idx_temp, columns=['mat_PG4_Recycling_target'], dtype=float)
df_glass_target.loc[2022] = 0
df_glass_target.loc[2030] = 100
df_glass_target.loc[2050] = 100
df_glass_target.interpolate(inplace=True)

df_glass_lowrecycle = pd.DataFrame(index=idx_temp, columns=['mat_EOL_Recycled_into_HQ'], dtype=float)
df_glass_lowrecycle.loc[2022] = 0
df_glass_lowrecycle.loc[2030] = 0
df_glass_lowrecycle.loc[2050] = 50
df_glass_lowrecycle.interpolate(inplace=True)

#eol
sim1.scenario['r_50PERC'].modifyMaterials('glass', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['r_50PERC'].modifyMaterials('glass', 'mat_PG4_Recycling_target', 
                                          df_glass_target.loc[2022:,'mat_PG4_Recycling_target'], start_year=2022) #send to recycle
#sim1.scenario['r_RecycledPERC'].modifyMaterials('glass', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['r_50PERC'].modifyMaterials('glass', 'mat_EOL_Recycled_into_HQ', 
                                          df_glass_lowrecycle.loc[2022:,'mat_EOL_Recycled_into_HQ'], start_year=2022) #all HQ
sim1.scenario['r_50PERC'].modifyMaterials('glass', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop


#silicon recycled
#create improvement series
idx_temp = pd.RangeIndex(start=2022,stop=2051,step=1) #create the index
df_si_target = pd.DataFrame(index=idx_temp, columns=['mat_PG4_Recycling_target'], dtype=float)
df_si_target.loc[2022] = 0
df_si_target.loc[2030] = 0
df_si_target.loc[2050] = 50
df_si_target.interpolate(inplace=True)

#eol
sim1.scenario['r_50PERC'].modifyMaterials('silicon', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['r_50PERC'].modifyMaterials('silicon', 'mat_PG4_Recycling_target', 
                                          df_si_target.loc[2022:,'mat_PG4_Recycling_target'], start_year=2022) #send to recycle
#sim1.scenario['r_50PERC'].modifyMaterials('silicon', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['r_50PERC'].modifyMaterials('silicon', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['r_50PERC'].modifyMaterials('silicon', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop


#sim1.scenario['r_50PERC'].material['silicon'].matdataIn_m


#aluminium_frames recycle
idx_temp = pd.RangeIndex(start=2022,stop=2051,step=1) #create the index
df_al_target = pd.DataFrame(index=idx_temp, columns=['mat_PG4_Recycling_target'], dtype=float)
df_al_target.loc[2022] = 0
df_al_target.loc[2030] = 100
df_al_target.loc[2050] = 100
df_al_target.interpolate(inplace=True)

df_al_lowrecycle = pd.DataFrame(index=idx_temp, columns=['mat_EOL_Recycled_into_HQ'], dtype=float)
df_al_lowrecycle.loc[2022] = 0
df_al_lowrecycle.loc[2030] = 100
df_al_lowrecycle.loc[2050] = 100
df_al_lowrecycle.interpolate(inplace=True)

df_al_lowCL = pd.DataFrame(index=idx_temp, columns=['mat_EOL_RecycledHQ_Reused4MFG'], dtype=float)
df_al_lowCL.loc[2022] = 0
df_al_lowCL.loc[2030] = 20
df_al_lowCL.loc[2050] = 50
df_al_lowCL.interpolate(inplace=True)

#eol
sim1.scenario['r_50PERC'].modifyMaterials('aluminium_frames', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['r_50PERC'].modifyMaterials('aluminium_frames', 'mat_PG4_Recycling_target', 
                                          df_al_target.loc[2022:,'mat_PG4_Recycling_target'], start_year=2022) #send to recycle
sim1.scenario['r_50PERC'].modifyMaterials('aluminium_frames', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['r_50PERC'].modifyMaterials('aluminium_frames', 'mat_EOL_Recycled_into_HQ', 
                                          df_al_lowrecycle.loc[2022:,'mat_EOL_Recycled_into_HQ'], start_year=2022) #all HQ
sim1.scenario['r_50PERC'].modifyMaterials('aluminium_frames', 'mat_EOL_RecycledHQ_Reused4MFG', 
                                          df_al_lowCL.loc[2022:,'mat_EOL_RecycledHQ_Reused4MFG'], start_year=2022) #closed-loop


#sim1.scenario['r_50PERC'].material['aluminium_frames'].matdataIn_m


#modified module
moduleinput_m_r_perovskite = os.path.join(altBaselinesfolder, 'mod_r_perovskite.csv')
sim1.createScenario(name='r_Perovskite', massmodulefile=moduleinput_m_r_perovskite, energymodulefile=moduleFile_e) 

#modified materials
#glass
matinput_glass_m_remfg = os.path.join(altBaselinesfolder,'material_mass_glass_r_perovskite.csv')
matbaseline_e_glass = os.path.join(baselinesfolder,'baseline_material_energy_glass.csv')
sim1.scenario['r_Perovskite'].addMaterial('glass', massmatfile=matinput_glass_m_remfg, energymatfile=matbaseline_e_glass)

#silicon
matinput_silicon_m_remfg = os.path.join(altBaselinesfolder,'material_mass_silicon_r_perovskite.csv')
matbaseline_e_si = os.path.join(baselinesfolder,'baseline_material_energy_silicon.csv')
sim1.scenario['r_Perovskite'].addMaterial('silicon', massmatfile=matinput_silicon_m_remfg, energymatfile=matbaseline_e_si)

#load standard materials
subMats =['silver', 'aluminium_frames','copper','encapsulant', 'backsheet']
for mat in range (0, len(subMats)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+subMats[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+subMats[mat]+'.csv')
    sim1.scenario['r_Perovskite'].addMaterial(subMats[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#aluminium_frames recycle
#eol
sim1.scenario['r_Perovskite'].modifyMaterials('aluminium_frames', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['r_Perovskite'].modifyMaterials('aluminium_frames', 'mat_PG4_Recycling_target', 100.0, start_year=2022) #send to recycle
sim1.scenario['r_Perovskite'].modifyMaterials('aluminium_frames', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['r_Perovskite'].modifyMaterials('aluminium_frames', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['r_Perovskite'].modifyMaterials('aluminium_frames', 'mat_EOL_RecycledHQ_Reused4MFG',100.0, start_year=2022) #closed-loop


#trim to start in 2000, this trims module and materials
#had to specify and end year, cannot use to extend
sim1.trim_Years(startYear=2000, endYear=2100)

Trimming and extending  PV_ICE
PV_ICE : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  ex_PERC_50
ex_PERC_50 : Data trimmed for Energy, years now encompass  2000  to  2100
ex_PERC_50 : Data trimmed for Energy, years now encompass  2000  to  2100
ex_PERC_50 : Data trimmed for Energy, years now encompass  2000  to  2100
ex_PERC_50 : Data trimmed for Energy, years now encompass  2000  to  2100
ex_PERC_50 : Data trimmed for Energy, years now encompass  2000  to  2100
ex_PERC_50 : Data trimmed for Energy, years now encompass  2000  to  2100
ex_PERC_50 : Data trimmed for Energy, years now encompass  2000  to  2100
ex_PERC_50 : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  ex_High_eff
ex_High_eff : Data trimmed for Energy, years now encompass  2000  to  2100
ex_High_eff : Data trimmed for Energy, years now encompass  2000  to  2100
ex_High_eff : Data trimmed for Energy, years now encompass  2000  to  2100
ex_High_eff : Data trimmed for Energy, years now encompass  2000  to  2100
ex_High_eff : Data trimmed for Energy, years now encompass  2000  to  2100
ex_High_eff : Data trimmed for Energy, years now encompass  2000  to  2100
ex_High_eff : Data trimmed for Energy, years now encompass  2000  to  2100
ex_High_eff : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  ex_Perovskite
ex_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Perovskite : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  ex_Lightweight
ex_Lightweight : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Lightweight : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Lightweight : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Lightweight : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Lightweight : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Lightweight : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Lightweight : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Lightweight : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  r_PERC
r_PERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_PERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_PERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_PERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_PERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_PERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_PERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_PERC : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  r_SHJ
r_SHJ : Data trimmed for Energy, years now encompass  2000  to  2100
r_SHJ : Data trimmed for Energy, years now encompass  2000  to  2100
r_SHJ : Data trimmed for Energy, years now encompass  2000  to  2100
r_SHJ : Data trimmed for Energy, years now encompass  2000  to  2100
r_SHJ : Data trimmed for Energy, years now encompass  2000  to  2100
r_SHJ : Data trimmed for Energy, years now encompass  2000  to  2100
r_SHJ : Data trimmed for Energy, years now encompass  2000  to  2100
r_SHJ : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  r_TOPCon
r_TOPCon : Data trimmed for Energy, years now encompass  2000  to  2100
r_TOPCon : Data trimmed for Energy, years now encompass  2000  to  2100
r_TOPCon : Data trimmed for Energy, years now encompass  2000  to  2100
r_TOPCon : Data trimmed for Energy, years now encompass  2000  to  2100
r_TOPCon : Data trimmed for Energy, years now encompass  2000  to  2100
r_TOPCon : Data trimmed for Energy, years now encompass  2000  to  2100
r_TOPCon : Data trimmed for Energy, years now encompass  2000  to  2100
r_TOPCon : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  r_IRENA
r_IRENA : Data trimmed for Energy, years now encompass  2000  to  2100
r_IRENA : Data trimmed for Energy, years now encompass  2000  to  2100
r_IRENA : Data trimmed for Energy, years now encompass  2000  to  2100
r_IRENA : Data trimmed for Energy, years now encompass  2000  to  2100
r_IRENA : Data trimmed for Energy, years now encompass  2000  to  2100
r_IRENA : Data trimmed for Energy, years now encompass  2000  to  2100
r_IRENA : Data trimmed for Energy, years now encompass  2000  to  2100
r_IRENA : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  r_RecycledPERC
r_RecycledPERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_RecycledPERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_RecycledPERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_RecycledPERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_RecycledPERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_RecycledPERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_RecycledPERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_RecycledPERC : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  r_50PERC
r_50PERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_50PERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_50PERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_50PERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_50PERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_50PERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_50PERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_50PERC : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  r_Perovskite
r_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
r_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
r_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
r_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
r_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
r_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
r_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
r_Perovskite : Data trimmed for Mass, years now encompass  2000  to  2100


#check
sim1.scenario['r_Perovskite'].dataIn_m


global_projection = pd.read_csv(os.path.join(supportMatfolder,'output-globalInstallsProjection.csv'), index_col=0)

fig, ax1 = plt.subplots()

ax1.stackplot(global_projection.index, global_projection['World_cum']/1e6, color='#F7A11A')
ax1.set_ylabel('Cumulative Solar Capacity [TW]', color='#F7A11A')
ax2 = ax1.twinx()
ax2.plot(global_projection['World_annual_[MWdc]']/1e6)
ax2.set_ylabel('Annual Installations [TW]')
plt.show()


#deployment projection for all scenarios
sim1.modifyScenario(scenarios=None,stage='new_Installed_Capacity_[MW]', 
                    value= global_projection['World_annual_[MWdc]'], start_year=2000)
#for scen in scennames:
#    sim1.scenario[scen].dataIn_m.loc[0:len(global_projection.index-1),'new_Installed_Capacity_[MW]'] = global_projection['World_annual_[MWdc]'].values


sim1.scenario.keys()

dict_keys(['PV_ICE', 'ex_PERC_50', 'ex_High_eff', 'ex_Perovskite', 'ex_Lightweight', 'r_PERC', 'r_SHJ', 'r_TOPCon', 'r_IRENA', 'r_RecycledPERC', 'r_50PERC', 'r_Perovskite'])


scennames_ex = ['ex_PERC_50', 'ex_High_eff', 'ex_Perovskite', 'ex_Lightweight'] #extreme cases
scennames_r = ['r_PERC', 'r_SHJ', 'r_TOPCon', 'r_IRENA', 'r_RecycledPERC', 'r_50PERC', 'r_Perovskite'] #realistic cases
scennames_all = ['ex_PERC_50', 'ex_High_eff', 'ex_Perovskite', 'ex_Lightweight',
                 'r_PERC', 'r_SHJ', 'r_TOPCon', 'r_IRENA', 'r_RecycledPERC', 'r_50PERC', 'r_Perovskite']


for scen in sim1.scenario.keys():
    print(sim1.scenario[scen].material.keys())

dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])


IRENAregloss = {'alpha':5.692,
                   'beta':29.697}

idx_temp = pd.RangeIndex(start=2022,stop=2101,step=1) #create the index
evolve_weibull = pd.DataFrame(index=idx_temp, columns=['alpha','beta'], dtype=float)
evolve_weibull.loc[2022] = [5.3759,30]
evolve_weibull.loc[2030] = [5.1   ,35]
evolve_weibull.loc[2050] = [4.602 ,40.767]

evolve_weibull.interpolate(inplace=True)
evolve_weibull = round(evolve_weibull,3)
#evolve_weibull_temp = evolve_weibull.reset_index(drop=True)
#evolve_weibull_temp.to_dict(orient='dict')
#dict([(i,[a,b,c ]) for i, a,b,c in zip(df.ID, df.A,df.B,df.C)])
dict_weibull = {'alpha':evolve_weibull['alpha'].tolist(),
                'beta':evolve_weibull['beta'].tolist()}
evolve_weibull.to_csv(os.path.join(altBaselinesfolder,'evolve_weibull.csv'))


sim1.calculateMassFlow()

>>>> Calculating Material Flows <<<<

Working on Scenario:  PV_ICE
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  ex_PERC_50
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  ex_High_eff
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  ex_Perovskite
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
ReMFG surplus End of Sim for Mat  glass  Scenario  ex_Perovskite  =  1517606899.8818028  tonnes.
Recycled surplus End of Sim for Mat  glass  Scenario  ex_Perovskite  =  11026448.096860003  tonnes.
==> Working on Material :  silicon
ReMFG surplus End of Sim for Mat  silicon  Scenario  ex_Perovskite  =  44955370.66368066  tonnes.
Recycled surplus End of Sim for Mat  silicon  Scenario  ex_Perovskite  =  267559.95619055326  tonnes.
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  ex_Perovskite  =  158195614.90356943  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  ex_Lightweight
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  ex_Lightweight  =  1120211.444206365  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  r_PERC
********************
Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  r_PERC  =  938961852.6670737  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  r_PERC  =  29146074.36401711  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  r_PERC  =  210873.41096709532  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  r_PERC  =  158404098.63113916  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  r_SHJ
********************
Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  r_SHJ  =  913256187.2391267  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  r_SHJ  =  28415128.522242736  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  r_SHJ  =  338624.3865555966  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  r_SHJ  =  154262301.74565554  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  r_TOPCon
********************
Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  r_TOPCon  =  922853674.8179162  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  r_TOPCon  =  28688680.44808312  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  r_TOPCon  =  314995.196957269  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  r_TOPCon  =  155807898.14842984  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  r_IRENA
********************
Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  r_IRENA  =  38959529998.722115  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  r_IRENA  =  1409773346.4111698  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  r_IRENA  =  8205688.481399452  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  r_IRENA  =  6468197305.673917  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  r_RecycledPERC
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  r_RecycledPERC  =  67266696.26591074  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  r_RecycledPERC  =  403789.03861587023  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  r_RecycledPERC  =  230037910.73027405  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  r_50PERC
********************
Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  r_50PERC  =  348384.44972485985  tonnes.
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  r_50PERC  =  10905234.451285902  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  r_Perovskite
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
ReMFG surplus End of Sim for Mat  glass  Scenario  r_Perovskite  =  21300468292.618156  tonnes.
Recycled surplus End of Sim for Mat  glass  Scenario  r_Perovskite  =  7944094.3365952615  tonnes.
==> Working on Material :  silicon
ReMFG surplus End of Sim for Mat  silicon  Scenario  r_Perovskite  =  521803917.27075243  tonnes.
Recycled surplus End of Sim for Mat  silicon  Scenario  r_Perovskite  =  15243971.624450725  tonnes.
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  r_Perovskite  =  2502781399.4497313  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet


sim1.calculateMassFlow(scenarios=['ex_Lightweight','r_IRENA'], weibullInputParams=IRENAregloss)
#need to create an upgrade to weibull params to account for evolving params
#dict_weibull # evolve_weibull
#sim1.calculateMassFlow(scenarios=['r_IRENA'])

>>>> Calculating Material Flows <<<<

Working on Scenario:  ex_Lightweight
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  ex_Lightweight  =  757850.2752181168  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  r_IRENA
********************
Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  r_IRENA  =  28724070549.778393  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  r_IRENA  =  1038959656.3829762  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  r_IRENA  =  6048701.2215623455  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  r_IRENA  =  4775358360.133832  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet


ii_yearly, ii_cumu = sim1.aggregateResults() #have to do this to get auto plots


sim1.scenario['r_IRENA'].dataOut_m['WeibullParams']

0      {'alpha': 5.692, 'beta': 29.697}
1      {'alpha': 5.692, 'beta': 29.697}
2      {'alpha': 5.692, 'beta': 29.697}
3      {'alpha': 5.692, 'beta': 29.697}
4      {'alpha': 5.692, 'beta': 29.697}
                     ...               
96     {'alpha': 5.692, 'beta': 29.697}
97     {'alpha': 5.692, 'beta': 29.697}
98     {'alpha': 5.692, 'beta': 29.697}
99     {'alpha': 5.692, 'beta': 29.697}
100    {'alpha': 5.692, 'beta': 29.697}
Name: WeibullParams, Length: 101, dtype: object


effective_capacity = ii_yearly.filter(like='ActiveCapacity')
plt.plot(ii_cumu['newInstalledCapacity_sim1_PV_ICE_[MW]']/1e6, label='Capacity Target', color='black', ls='--')
plt.plot(effective_capacity/1e6, label=sim1.scenario.keys())
plt.legend()
plt.ylabel('Effective Capacity [TW]')
plt.title('Effective Capacity: No Replacements')
plt.ylim(0,)

(0.0, 64.01425956655)


effective_capacity = ii_yearly.filter(like='ActiveCapacity').filter(like='r_')
plt.plot(ii_cumu['newInstalledCapacity_sim1_PV_ICE_[MW]']/1e6, label='Capacity Target', color='black', ls='--')
plt.plot(effective_capacity/1e6, label=scennames_r)
plt.legend()
plt.ylabel('Effective Capacity [TW]')
plt.title('Effective Capacity: No Replacements')
plt.ylim(0,)

(0.0, 64.01425956655)


sim1.scenario['r_PERC'].dataIn_m['year'].values+sim1.scenario['r_PERC'].dataIn_m['mod_lifetime'].values

array([2020.   , 2021.   , 2022.   , 2023.   , 2024.   , 2025.   ,
       2026.   , 2028.5  , 2029.5  , 2030.6  , 2032.7  , 2034.   ,
       2035.5  , 2037.2  , 2040.   , 2043.7  , 2044.7  , 2046.5  ,
       2049.   , 2051.4  , 2055.   , 2056.   , 2052.   , 2053.625,
       2055.25 , 2056.875, 2058.5  , 2060.125, 2061.75 , 2063.375,
       2065.   , 2066.15 , 2067.3  , 2068.45 , 2069.6  , 2070.75 ,
       2071.9  , 2073.05 , 2074.2  , 2075.35 , 2076.5  , 2077.65 ,
       2078.8  , 2079.95 , 2081.1  , 2082.25 , 2083.4  , 2084.55 ,
       2085.7  , 2086.85 , 2088.   , 2089.   , 2090.   , 2091.   ,
       2092.   , 2093.   , 2094.   , 2095.   , 2096.   , 2097.   ,
       2098.   , 2099.   , 2100.   , 2101.   , 2102.   , 2103.   ,
       2104.   , 2105.   , 2106.   , 2107.   , 2108.   , 2109.   ,
       2110.   , 2111.   , 2112.   , 2113.   , 2114.   , 2115.   ,
       2116.   , 2117.   , 2118.   , 2119.   , 2120.   , 2121.   ,
       2122.   , 2123.   , 2124.   , 2125.   , 2126.   , 2127.   ,
       2128.   , 2129.   , 2130.   , 2131.   , 2132.   , 2133.   ,
       2134.   , 2135.   , 2136.   , 2137.   , 2138.   ])


plt.plot(ii_yearly.filter(like='Decommisioned'))

[<matplotlib.lines.Line2D at 0x1359a1d82e0>,
 <matplotlib.lines.Line2D at 0x1359a1d8340>,
 <matplotlib.lines.Line2D at 0x1359a1d8460>,
 <matplotlib.lines.Line2D at 0x1359a1d8580>,
 <matplotlib.lines.Line2D at 0x1359a1d86a0>,
 <matplotlib.lines.Line2D at 0x1359a1d87c0>,
 <matplotlib.lines.Line2D at 0x1359a1d88e0>,
 <matplotlib.lines.Line2D at 0x1359a1d8a00>,
 <matplotlib.lines.Line2D at 0x1359a1d8b20>,
 <matplotlib.lines.Line2D at 0x1359a1d8c40>,
 <matplotlib.lines.Line2D at 0x1359a1d8d60>,
 <matplotlib.lines.Line2D at 0x1359a1d8e80>]


effective_capacity = ii_yearly.filter(like='ActiveCapacity').filter(like='ex_')
plt.plot(ii_cumu['newInstalledCapacity_sim1_PV_ICE_[MW]']/1e6, label='Capacity Target', color='black', ls='--')
plt.plot(effective_capacity/1e6, label=scennames_ex)
plt.legend()
plt.ylabel('Effective Capacity [TW]')
plt.title('Effective Capacity: No Replacements')
plt.ylim(0,)

(0.0, 64.01425956655)


bifiFactors = {'ex_PERC_50':0.7, # ITRPV 2022, Fig. 58
               'ex_High_eff': 0.9,
               'ex_Perovskite': 0.0,
               'ex_Lightweight':0.0,
               'r_PERC':0.7,
               'r_SHJ':0.9, # ITRPV 2022, Fig. 58
               'r_TOPCon':0.8, # ITRPV 2022, Fig. 58
               'r_Perovskite':0.0,
               'r_RecycledPERC':0.6,
                'PV_ICE':0.0,
                'r_IRENA':0.0,
                'r_50PERC':0.7} 

#MAY NEED TO CHANGE TO BE DYNAMIC


#PV ICE currently set up to read in a csv of bifi factors, so generate files to read in 
idx_temp = pd.RangeIndex(start=2000,stop=2101,step=1) #create the index
df_temp = pd.DataFrame(index=idx_temp, columns=['bifi'], dtype=float)
bifiPathDict={}

for f in bifiFactors.keys(): #loop over module types
    bifi = df_temp.copy() #copy of df
    bifi['bifi'] = bifiFactors[f] #assign column
    bifipath = os.path.join(testfolder,'bifi_'+str(f)+'.csv') #create file path
    bifi.to_csv(path_or_buf=bifipath, index_label='year') #create file
    bifiPathDict[str(f)] = bifipath
    #append bifi path to dict? or list?


#currently takes ~40 mins to run with 7 materials and 12 scenarios

for row in range (0,len(sim1.scenario['PV_ICE'].dataIn_m)): #loop over length of years
    print(row)
    for scen in sim1.scenario.keys(): #loop over scenarios
        print(scen)
        Under_Installment = global_projection.iloc[row,0] - ((sim1.scenario[scen].dataOut_m['Installed_Capacity_[W]'][row])/1e6)  # MWATTS
        sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed
        #calculate flows for that scenario with it's bifi factor and modified weibull
        if scen in ['r_IRENA','ex_Lightweight']:
            sim1.calculateFlows(scenarios=[scen], bifacialityfactors=bifiPathDict[scen], weibullInputParams=IRENAregloss)
        else:
            sim1.calculateFlows(scenarios=[scen], bifacialityfactors=bifiPathDict[scen])

0
PV_ICE
>>>> Calculating Material Flows <<<<

Working on Scenario:  PV_ICE
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_8224\3025388339.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet


>>>> Calculating Energy Flows <<<<

Working on Scenario:  PV_ICE
********************
==> Working on Energy for Material :  glass
==> Working on Energy for Material :  silicon
==> Working on Energy for Material :  silver
==> Working on Energy for Material :  aluminium_frames
==> Working on Energy for Material :  copper
==> Working on Energy for Material :  encapsulant
==> Working on Energy for Material :  backsheet
ex_PERC_50
>>>> Calculating Material Flows <<<<

Working on Scenario:  ex_PERC_50
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_8224\3025388339.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet


>>>> Calculating Energy Flows <<<<

Working on Scenario:  ex_PERC_50
********************
==> Working on Energy for Material :  glass
==> Working on Energy for Material :  silicon
==> Working on Energy for Material :  silver
==> Working on Energy for Material :  aluminium_frames
==> Working on Energy for Material :  copper
==> Working on Energy for Material :  encapsulant
==> Working on Energy for Material :  backsheet
ex_High_eff
>>>> Calculating Material Flows <<<<

Working on Scenario:  ex_High_eff
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_8224\3025388339.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet


>>>> Calculating Energy Flows <<<<

Working on Scenario:  ex_High_eff
********************
==> Working on Energy for Material :  glass
==> Working on Energy for Material :  silicon
==> Working on Energy for Material :  silver
==> Working on Energy for Material :  aluminium_frames
==> Working on Energy for Material :  copper
==> Working on Energy for Material :  encapsulant
==> Working on Energy for Material :  backsheet
ex_Perovskite
>>>> Calculating Material Flows <<<<

Working on Scenario:  ex_Perovskite
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_8224\3025388339.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
ReMFG surplus End of Sim for Mat  glass  Scenario  ex_Perovskite  =  1517606899.8818028  tonnes.
Recycled surplus End of Sim for Mat  glass  Scenario  ex_Perovskite  =  11026448.096860003  tonnes.
==> Working on Material :  silicon
ReMFG surplus End of Sim for Mat  silicon  Scenario  ex_Perovskite  =  44955370.66368066  tonnes.
Recycled surplus End of Sim for Mat  silicon  Scenario  ex_Perovskite  =  267559.95619055326  tonnes.
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  ex_Perovskite  =  158195614.90356943  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet


>>>> Calculating Energy Flows <<<<

Working on Scenario:  ex_Perovskite
********************
==> Working on Energy for Material :  glass
==> Working on Energy for Material :  silicon
==> Working on Energy for Material :  silver
==> Working on Energy for Material :  aluminium_frames
==> Working on Energy for Material :  copper
==> Working on Energy for Material :  encapsulant
==> Working on Energy for Material :  backsheet
ex_Lightweight
>>>> Calculating Material Flows <<<<

Working on Scenario:  ex_Lightweight
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_8224\3025388339.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed


Under_Installment

3018107.5775131136


#aggregate results - mass
cc_yearly, cc_cumu = sim1.aggregateResults() #have to do this to get auto plots
cc_yearly.to_csv(os.path.join(testfolder, 'cc_12scen_yearly.csv'))
cc_cumu.to_csv(os.path.join(testfolder, 'cc_12scen_cumu.csv'))


#aggregate results - energy
allenergy, energyGen, energy_demands = sim1.aggregateEnergyResults()
allenergy.to_csv(os.path.join(testfolder, 'cc_12scen_allenergy.csv'))
energyGen.to_csv(os.path.join(testfolder, 'cc_12scen_energyGen.csv'))
energy_demands.to_csv(os.path.join(testfolder, 'cc_12scen_energy_demands.csv'))

C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2056: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2056: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2056: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2056: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2056: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2056: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2056: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2056: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2056: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2056: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2056: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2056: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)


#read in saved results files for speed
cc_yearly = pd.read_csv(os.path.join(testfolder, 'cc_12scen_yearly.csv'), index_col='year')
cc_cumu = pd.read_csv(os.path.join(testfolder, 'cc_12scen_cumu.csv'), index_col='year')
allenergy = pd.read_csv(os.path.join(testfolder, 'cc_12scen_allenergy.csv'), index_col='year')
energyGen = pd.read_csv(os.path.join(testfolder, 'cc_12scen_energyGen.csv'), index_col='year')
energy_demands = pd.read_csv(os.path.join(testfolder, 'cc_12scen_energy_demands.csv'), index_col='year')


plt.plot(sim1.scenario['r_PERC'].dataOut_m['Installed_Capacity_[W]'])

[<matplotlib.lines.Line2D at 0x135985773d0>]


#sim1.scenario['PV_ICE'].dataOut_m['irradiance_stc'].head(2)
sim1.scenario.keys()

dict_keys(['PV_ICE', 'ex_PERC_50', 'ex_High_eff', 'ex_Perovskite', 'ex_Lightweight', 'r_PERC', 'r_SHJ', 'r_TOPCon', 'r_IRENA', 'r_RecycledPERC', 'r_50PERC', 'r_Perovskite'])


sim1.scenario['r_RecycledPERC'].dataOut_m['WeibullParams'].tail(4)

97     {'alpha': 12.596192434998898, 'beta': 41.18098...
98     {'alpha': 12.596192434998898, 'beta': 41.18098...
99     {'alpha': 12.596192434998898, 'beta': 41.18098...
100    {'alpha': 12.596192434998898, 'beta': 41.18098...
Name: WeibullParams, dtype: object


effective_capacity = cc_yearly.filter(like='ActiveCapacity')
plt.plot(effective_capacity/1e6)
plt.legend(sim1.scenario.keys())
plt.ylabel('Effective Capacity [TW]')
plt.title('Effective Capacity: With Replacements')
plt.ylim(0,)

(0.0, 64.01425956655)


annual_EoL = cc_yearly.filter(like='DecommisionedCapacity')
plt.plot(annual_EoL/1e6)
plt.legend(sim1.scenario.keys())
plt.ylabel('Annual EoL [TW]')
plt.title('Annual Decommissions [TW]')
plt.ylim(0,)

(0.0, 264.94150757286445)


annual_installs = cc_yearly.filter(like='newInstalled')
plt.plot(annual_installs/1e6)
plt.legend(sim1.scenario.keys())
plt.ylabel('Annual installed [TW]')
plt.title('Annual Installs with Replacements')
plt.ylim(0,)

(0.0, 6.238887660174118)


cumu_installs_annually = cc_cumu.filter(like='newInstalled')
plt.plot(cumu_installs_annually/1e6)
plt.legend(sim1.scenario.keys())
plt.ylabel('Cumulative installed [TW]')
plt.title('Replacements Adjusted Deployment Curve \n Cumulative Installs with Replacements')
plt.ylim(0,)

(0.0, 328.9557671394145)


cumu_installs


cumu_installs = cc_cumu.filter(like='newInstalled')
plt.bar(scennames_labels, cumu_installs.loc[2100]/1e6, tick_label=scennames_labels, color=colorpalette)
#plt.legend(scennames)
plt.xticks(rotation=90)
plt.ylabel('Cumulative installed [TW]')
plt.title('Cumulative Installs with Replacements')

Text(0.5, 1.0, 'Cumulative Installs with Replacements')


cumulative_nameplate_installs = global_projection.loc[2100,'World_cum'] #MW
print('The nameplate installations for energy transition and through 2100 are '+str(cumulative_nameplate_installs/1e6)+' TW.')

The nameplate installations for energy transition and through 2100 are 60.966 TW.


global_projection['World_annual_[MWdc]'].sum()

60966000.00000001


Additional_installs = cumu_installs.loc[2100]-global_projection.loc[2100,'World_cum']
plt.bar(scennames_labels, Additional_installs/1e6, tick_label=scennames_labels, color=colorpalette)
#plt.legend(scennames)
plt.ylabel('Cumulative Replacements [TW]')
plt.title('Replacements Required by Technology')
plt.xticks(rotation=90)

(array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.]),
 [Text(0.0, 0, 'PV_ICE'),
  Text(1.0, 0, '50-year'),
  Text(2.0, 0, 'High Eff'),
  Text(3.0, 0, 'Idealized\nPerovskite\nSi-Tandem'),
  Text(4.0, 0, 'Lightweight'),
  Text(5.0, 0, 'PERC'),
  Text(6.0, 0, 'SHJ'),
  Text(7.0, 0, 'TOPCon'),
  Text(8.0, 0, 'IRENA\nreg. loss'),
  Text(9.0, 0, 'Recycled\nPERC'),
  Text(10.0, 0, 'long-lived\nPERC'),
  Text(11.0, 0, 'Hopeful\nPerovskite')])


cumu_virgin_module = cc_cumu.filter(like='VirginStock_Module')
plt.bar(scennames_labels, cumu_virgin_module.loc[2100]/1e9, tick_label=scennames_labels, color=colorpalette)
#plt.legend(scennames)
plt.title('Cumulative Virgin Material Demands')
plt.ylabel('Virgin Material Requirements\n[billion tonnes]')
plt.xticks(rotation=90)

(array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.]),
 [Text(0.0, 0, 'PV_ICE'),
  Text(1.0, 0, '50-year'),
  Text(2.0, 0, 'High Eff'),
  Text(3.0, 0, 'Idealized\nPerovskite\nSi-Tandem'),
  Text(4.0, 0, 'Lightweight'),
  Text(5.0, 0, 'PERC'),
  Text(6.0, 0, 'SHJ'),
  Text(7.0, 0, 'TOPCon'),
  Text(8.0, 0, 'IRENA\nreg. loss'),
  Text(9.0, 0, 'Recycled\nPERC'),
  Text(10.0, 0, 'long-lived\nPERC'),
  Text(11.0, 0, 'Hopeful\nPerovskite')])


recycledperc_virginstock = cc_cumu.filter(like='VirginStock').filter(like='Recycled')
recycledperc_virginstock.drop('VirginStock_Module_sim1_r_RecycledPERC_[Tonnes]',axis=1, inplace=True)
plt.bar(recycledperc_virginstock.columns, recycledperc_virginstock.loc[2100]/1e6, tick_label = MATERIALS, color=colormats)
plt.ylabel('Million Metric tonnes')

Text(0, 0.5, 'Million Metric tonnes')


cumu_virgin_module = cc_cumu.filter(like='VirginStock_Module')
plt.plot(cumu_virgin_module/1e9)
plt.legend(scennames_labels)
plt.title('Cumulative Virgin Material Extraction: Module')
plt.ylabel('Virgin Material Requirements\n[billion tonnes]')
plt.ylim(0,)

(0.0, 11.399771392641203)


annual_virgin_module = cc_yearly.filter(like='VirginStock_Module')
plt.plot(annual_virgin_module/1e6)
plt.legend(scennames_labels)
plt.title('Annual Virgin Material Extraction: Module')
plt.ylabel('Virgin Material Requirements\n[million tonnes]')
plt.ylim(0,)

(0.0, 251.3943849778702)


#print out masses for stacked bar charts
#mass_cumu = cc_cumu.loc[[2100]].filter(like='PV_ICE_[Tonnes]')
mass_cumu_virgin = cc_cumu.filter(like='VirginStock').loc[2100]
mass_cumu_wasteall = cc_cumu.filter(like='WasteAll').loc[2100]
mass_cumu = pd.concat([mass_cumu_virgin,mass_cumu_wasteall])
mass_cumu.to_csv(os.path.join(testfolder, 'cc_cumu_mass_2100results.csv'))


cc_cumu.to_csv(os.path.join(testfolder, 'cc_cumu_mass_results.csv'))


cumu_lifecycle_wastes = cc_cumu.filter(like='WasteAll_Module')

plt.bar(scennames_labels, cumu_lifecycle_wastes.loc[2100]/1e6, 
        tick_label=scennames_labels, color=colorpalette)
#plt.legend(scennames)
plt.title('Cumulative Lifecycle Wastes')
plt.ylabel('Lifecycle Wastes\n[million tonnes]')
plt.xticks(rotation=90)

(array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.]),
 [Text(0.0, 0, 'PV_ICE'),
  Text(1.0, 0, '50-year'),
  Text(2.0, 0, 'High Eff'),
  Text(3.0, 0, 'Idealized\nPerovskite\nSi-Tandem'),
  Text(4.0, 0, 'Lightweight'),
  Text(5.0, 0, 'PERC'),
  Text(6.0, 0, 'SHJ'),
  Text(7.0, 0, 'TOPCon'),
  Text(8.0, 0, 'IRENA\nreg. loss'),
  Text(9.0, 0, 'Recycled\nPERC'),
  Text(10.0, 0, 'long-lived\nPERC'),
  Text(11.0, 0, 'Hopeful\nPerovskite')])


sim1.scenario['r_SHJ'].dataOut_e


#energyGen = allenergy.filter(like='e_out_annual')
plt.plot(energyGen/1e12)
plt.legend(scennames_labels)
plt.title('Annual Energy Generation')
plt.ylabel('Energy Generation [TWh]')
plt.ylim(0,)

(0.0, 103909.74394690627)


e_annual_sumDemands = energy_demands.filter(like='demand_total')


plt.plot(e_annual_sumDemands/1e12)
plt.legend(scennames_labels)
plt.title('Annual Lifecycle Energy Demands')
plt.ylabel('Energy Demands\n[TWh]')
plt.ylim(0,)

(0.0, 32966.310772636556)


e_annual_sumDemands_cumu = e_annual_sumDemands.cumsum()


plt.bar(e_annual_sumDemands_cumu.columns, e_annual_sumDemands_cumu.loc[2100]/1e12, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Cumulative Lifecycle Energy Demands')
plt.ylabel('Cumulative Energy Demands\n[TWh]')
plt.xticks(rotation=90)

(array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.]),
 [Text(0.0, 0, 'PV_ICE'),
  Text(1.0, 0, '50-year'),
  Text(2.0, 0, 'High Eff'),
  Text(3.0, 0, 'Idealized\nPerovskite\nSi-Tandem'),
  Text(4.0, 0, 'Lightweight'),
  Text(5.0, 0, 'PERC'),
  Text(6.0, 0, 'SHJ'),
  Text(7.0, 0, 'TOPCon'),
  Text(8.0, 0, 'IRENA\nreg. loss'),
  Text(9.0, 0, 'Recycled\nPERC'),
  Text(10.0, 0, 'long-lived\nPERC'),
  Text(11.0, 0, 'Hopeful\nPerovskite')])


e_fuels = energy_demands.filter(like='_fuel')
e_energydemands = energy_demands.loc[:,~energy_demands.columns.isin(e_fuels.columns)]
e_mfging_bymat = pd.DataFrame()
for scen in sim1.scenario.keys():
    e_energy_mfg_scen = e_energydemands.filter(like=scen).filter(like='_MFG_')
    e_energy_mod_mfg_scen = energy_demands.filter(like=scen).filter(like='mod_MFG')
    e_mfging_bymat = pd.concat([e_mfging_bymat,e_energy_mfg_scen,e_energy_mod_mfg_scen], axis=1)


cum_e_mfging_bymat = e_mfging_bymat.cumsum().loc[2100]
cum_e_mfging_bymat

PV_ICE_glass_mat_MFG_virgin                     1.976820e+16
PV_ICE_silicon_mat_MFG_virgin                   4.380177e+16
PV_ICE_silver_mat_MFG_virgin                    3.307698e+14
PV_ICE_aluminium_frames_mat_MFG_virgin          1.393535e+16
PV_ICE_copper_mat_MFG_virgin                    6.901448e+13
                                                    ...     
r_Perovskite_aluminium_frames_mat_MFG_virgin    3.290855e+15
r_Perovskite_copper_mat_MFG_virgin              9.258170e+13
r_Perovskite_encapsulant_mat_MFG_virgin         2.059654e+15
r_Perovskite_backsheet_mat_MFG_virgin           2.577851e+15
r_Perovskite_mod_MFG                            2.445697e+15
Name: 2100, Length: 96, dtype: float64


cume_mfg_glass.values+cume_mfg_silicon.values

array([6.35699726e+16, 4.94960686e+16, 9.77591522e+16, 2.57919442e+16,
       2.04755804e+16, 4.22589456e+16, 4.01468838e+16, 4.10142656e+16,
       2.98274701e+16, 3.42184142e+16, 4.57011522e+16, 1.82794624e+16])


#energy demands by material mfging
plt.bar(e_annual_sumDemands_cumu.columns, e_annual_sumDemands_cumu.loc[2100], 
        tick_label=(scennames_labels), color=colorpalette)

#Materials
cume_mfg_glass = cum_e_mfging_bymat.filter(like='glass')
cume_mfg_silicon = cum_e_mfging_bymat.filter(like='silicon')
cume_mfg_silver = cum_e_mfging_bymat.filter(like='silver')
cume_mfg_aluminium_frames = cum_e_mfging_bymat.filter(like='aluminium_frames')
cume_mfg_copper = cum_e_mfging_bymat.filter(like='copper')
cume_mfg_encapsulant = cum_e_mfging_bymat.filter(like='encapsulant')
cume_mfg_backsheet = cum_e_mfging_bymat.filter(like='backsheet')
cume_mfg_mod = cum_e_mfging_bymat.filter(like='mod')

bot_ag = cume_mfg_glass.values+cume_mfg_silicon.values
bot_al = cume_mfg_glass.values+cume_mfg_silicon.values+cume_mfg_silver.values
bot_cu = cume_mfg_glass.values+cume_mfg_silicon.values+cume_mfg_silver.values+cume_mfg_aluminium_frames.values
bot_encap = cume_mfg_glass.values+cume_mfg_silicon.values+cume_mfg_silver.values+cume_mfg_aluminium_frames.values+cume_mfg_copper.values
bot_back = cume_mfg_glass.values+cume_mfg_silicon.values+cume_mfg_silver.values+cume_mfg_aluminium_frames.values+cume_mfg_copper.values+cume_mfg_encapsulant.values
bot_mod = cume_mfg_glass.values+cume_mfg_silicon.values+cume_mfg_silver.values+cume_mfg_aluminium_frames.values+cume_mfg_copper.values+cume_mfg_encapsulant.values+cume_mfg_backsheet.values

plt.bar(scennames_labels, cume_mfg_glass, color = colormats[0])
plt.bar(scennames_labels, cume_mfg_silicon, bottom = cume_mfg_glass, color=colormats[1])
plt.bar(scennames_labels, cume_mfg_silver, bottom = bot_ag, color=colormats[2])
plt.bar(scennames_labels, cume_mfg_aluminium_frames, bottom = bot_al, color=colormats[3])
plt.bar(scennames_labels, cume_mfg_copper, bottom = bot_cu , color=colormats[4])
plt.bar(scennames_labels, cume_mfg_encapsulant, bottom = bot_encap ,color=colormats[5])
plt.bar(scennames_labels, cume_mfg_backsheet, bottom = bot_back, color=colormats[6])
plt.bar(scennames_labels, cume_mfg_mod, bottom = bot_back, color='white')


plt.legend(MATERIALS)

<matplotlib.legend.Legend at 0x135a6966190>


energyGen_cumu = energyGen.cumsum()
energyGen_cumu.columns = e_annual_sumDemands_cumu.columns = scennames_labels
netEnergy_cumu = energyGen_cumu.loc[[2100]] - e_annual_sumDemands_cumu.loc[[2100]]


e_annual_sumDemands_cumu.loc[[2100]]


netEnergy_cumu


plt.bar(netEnergy_cumu.columns, netEnergy_cumu.loc[2100]/1e12, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Net Energy Cumulatively')
plt.ylabel('Cumulative Net Energy [TWh]')
plt.ylim(4e6,5.5e6)
plt.xticks(rotation=90)

(array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.]),
 [Text(0.0, 0, 'PV_ICE'),
  Text(1.0, 0, '50-year'),
  Text(2.0, 0, 'High Eff'),
  Text(3.0, 0, 'Idealized\nPerovskite\nSi-Tandem'),
  Text(4.0, 0, 'Lightweight'),
  Text(5.0, 0, 'PERC'),
  Text(6.0, 0, 'SHJ'),
  Text(7.0, 0, 'TOPCon'),
  Text(8.0, 0, 'IRENA\nreg. loss'),
  Text(9.0, 0, 'Recycled\nPERC'),
  Text(10.0, 0, 'long-lived\nPERC'),
  Text(11.0, 0, 'Hopeful\nPerovskite')])


netEnergy_relative = netEnergy_cumu - netEnergy_cumu.loc[2100,'PV_ICE']
netEnergy_relative
plt.bar(netEnergy_relative.columns, netEnergy_relative.loc[2100]/1e12, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Cumulatively Net Energy Relative to PV ICE')
plt.ylabel('Relative Cumulative Net Energy [TWh]')
plt.xticks(rotation=90)

(array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.]),
 [Text(0.0, 0, 'PV_ICE'),
  Text(1.0, 0, '50-year'),
  Text(2.0, 0, 'High Eff'),
  Text(3.0, 0, 'Idealized\nPerovskite\nSi-Tandem'),
  Text(4.0, 0, 'Lightweight'),
  Text(5.0, 0, 'PERC'),
  Text(6.0, 0, 'SHJ'),
  Text(7.0, 0, 'TOPCon'),
  Text(8.0, 0, 'IRENA\nreg. loss'),
  Text(9.0, 0, 'Recycled\nPERC'),
  Text(10.0, 0, 'long-lived\nPERC'),
  Text(11.0, 0, 'Hopeful\nPerovskite')])


netEnergy_cumu_norm = netEnergy_cumu/netEnergy_cumu.loc[2100,'PV_ICE']
netEnergy_cumu_norm_waterfall = netEnergy_cumu_norm-1
netEnergy_cumu_norm


plt.bar(netEnergy_cumu.columns, netEnergy_cumu_norm_waterfall.loc[2100], 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Net Energy Cumulatively Relative to PV ICE')
plt.ylabel('Relative Cumulative Net Energy')
#plt.ylim(-0.026,0.005)
plt.plot(0.0, lw=2)
plt.xticks(rotation=90)

(array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.]),
 [Text(0.0, 0, 'PV_ICE'),
  Text(1.0, 0, '50-year'),
  Text(2.0, 0, 'High Eff'),
  Text(3.0, 0, 'Idealized\nPerovskite\nSi-Tandem'),
  Text(4.0, 0, 'Lightweight'),
  Text(5.0, 0, 'PERC'),
  Text(6.0, 0, 'SHJ'),
  Text(7.0, 0, 'TOPCon'),
  Text(8.0, 0, 'IRENA\nreg. loss'),
  Text(9.0, 0, 'Recycled\nPERC'),
  Text(10.0, 0, 'long-lived\nPERC'),
  Text(11.0, 0, 'Hopeful\nPerovskite')])


plt.bar(netEnergy_cumu.columns, netEnergy_cumu.loc[2100]/1e12, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Net Energy Cumulatively')
plt.ylabel('Cumulative Net Energy\n[TWh]')
plt.xticks(rotation=90)

(array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.]),
 [Text(0.0, 0, 'PV_ICE'),
  Text(1.0, 0, '50-year'),
  Text(2.0, 0, 'High Eff'),
  Text(3.0, 0, 'Idealized\nPerovskite\nSi-Tandem'),
  Text(4.0, 0, 'Lightweight'),
  Text(5.0, 0, 'PERC'),
  Text(6.0, 0, 'SHJ'),
  Text(7.0, 0, 'TOPCon'),
  Text(8.0, 0, 'IRENA\nreg. loss'),
  Text(9.0, 0, 'Recycled\nPERC'),
  Text(10.0, 0, 'long-lived\nPERC'),
  Text(11.0, 0, 'Hopeful\nPerovskite')])


energyGen.columns = e_annual_sumDemands.columns = scennames_labels
annual_net_energy = energyGen - e_annual_sumDemands


plt.plot(annual_net_energy/1e12)
plt.legend(scennames_labels)
plt.title('Annual Net Energy')
plt.ylabel('Net Energy (Eout - Ein)\n[TWh]')
plt.ylim(0,100000)
plt.xlim(2000,2100)

(2000.0, 2100.0)


eroi_sys_cumu = energyGen_cumu/e_annual_sumDemands_cumu


plt.bar(eroi_sys_cumu.columns, eroi_sys_cumu.loc[2100], 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('EROI of all Deployments')
plt.ylabel('Unitless')
plt.xticks(rotation=90)

(array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.]),
 [Text(0.0, 0, 'PV_ICE'),
  Text(1.0, 0, '50-year'),
  Text(2.0, 0, 'High Eff'),
  Text(3.0, 0, 'Idealized\nPerovskite\nSi-Tandem'),
  Text(4.0, 0, 'Lightweight'),
  Text(5.0, 0, 'PERC'),
  Text(6.0, 0, 'SHJ'),
  Text(7.0, 0, 'TOPCon'),
  Text(8.0, 0, 'IRENA\nreg. loss'),
  Text(9.0, 0, 'Recycled\nPERC'),
  Text(10.0, 0, 'long-lived\nPERC'),
  Text(11.0, 0, 'Hopeful\nPerovskite')])


#cumulatively
netEnergy_cumu.columns = cumu_virgin_module.columns = cumu_lifecycle_wastes.columns = scennames_labels

CEETmetric = (netEnergy_cumu.loc[2100]/1e6)/ (cumu_virgin_module.loc[2100]+ cumu_lifecycle_wastes.loc[2100])


plt.bar(CEETmetric.index, CEETmetric, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('CETE Metric')
plt.ylabel('Net Energy / Linear Mass \n[MWh/tonne]')


single_deploy_2022 = pd.DataFrame(index=idx_temp, columns=['MW'], dtype=float)
single_deploy_2022['MW'] = 0.0
single_deploy_2022.loc[2022] = 100.0 #install a single 100 MW system


sim2 = PV_ICE.Simulation(name='sim2', path=testfolder)
for scen in scennames:
    sim2.createScenario(name=scen, massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
    for mat in range (0, len(MATERIALS)):
        matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
        matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
        sim2.scenario[scen].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#use the settings from the first simulation to create the identical settings for sim2
for scen in scennames:
    sim2.scenario[scen].dataIn_m = sim1.scenario[scen].dataIn_m
    sim2.scenario[scen].dataIn_e = sim1.scenario[scen].dataIn_e
    for mat in MATERIALS:
        sim2.scenario[scen].material[mat].matdataIn_m = sim1.scenario[scen].material[mat].matdataIn_m
        sim2.scenario[scen].material[mat].matdataIn_e = sim1.scenario[scen].material[mat].matdataIn_e


sim2.scenario['Perovskite'].material['glass'].matdataIn_m


#trim to start in 2000, this trims module and materials
#had to specify and end year, cannot use to extend
sim2.trim_Years(startYear=2000, endYear=2100)


#deployment projection for all scenarios
sim2.modifyScenario(scenarios=None,stage='new_Installed_Capacity_[MW]', value= single_deploy_2022['MW'], start_year=2000)


sim2.scenario['Perovskite'].dataIn_m.loc[19:25]


for scen in scennames:
    sim2.calculateFlows(scenarios=[scen],bifacialityfactors=bifiPathDict[scen])


sim2_yearly, sim2_cumulative = sim2.aggregateResults()


allenergy2, energyGen2, energy_demands2 = sim2.aggregateEnergyResults()


allenergy2_cumu = allenergy2.cumsum()
energyGen2_cumu = energyGen2.cumsum()
energy_demands2_cumu = energy_demands2.cumsum()


energy_demands2_cumu_total = energy_demands2_cumu.filter(like='demand_total')
#energy_demands2_cumu_total


plt.bar(energy_demands2_cumu_total.columns, energy_demands2_cumu_total.loc[2100], 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Energy Demands Cumulative')
plt.ylabel('Energy Demands [Wh]')


energyGen2_cumu.columns=energy_demands2_cumu_total.columns=scennames_labels
eroi_2022 = energyGen2_cumu.iloc[100]/energy_demands2_cumu_total.iloc[100]
eroi_2022


plt.bar(eroi_2022.index, eroi_2022, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('EROI')
plt.ylabel('Arb.')


plt.plot(energyGen2/1e9)
plt.legend(scennames_labels)
plt.title('Energy Generation - Single 2022 System')
plt.ylabel('[GWh]')


single_deploy_2050 = pd.DataFrame(index=idx_temp, columns=['MW'], dtype=float)
single_deploy_2050['MW'] = 0.0
single_deploy_2050.loc[2050] = 100.0 #install a single 100 MW system


sim3 = PV_ICE.Simulation(name='sim3', path=testfolder)
for scen in scennames:
    sim3.createScenario(name=scen, massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
    for mat in range (0, len(MATERIALS)):
        matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
        matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
        sim3.scenario[scen].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#use the settings from the first simulation to create the identical settings for sim2
for scen in scennames:
    sim3.scenario[scen].dataIn_m = sim1.scenario[scen].dataIn_m
    sim3.scenario[scen].dataIn_e = sim1.scenario[scen].dataIn_e
    for mat in MATERIALS:
        sim3.scenario[scen].material[mat].matdataIn_m = sim1.scenario[scen].material[mat].matdataIn_m
        sim3.scenario[scen].material[mat].matdataIn_e = sim1.scenario[scen].material[mat].matdataIn_e


sim3.scenario['Perovskite'].material['glass'].matdataIn_m


#deployment projection for all scenarios
sim3.modifyScenario(scenarios=None,stage='new_Installed_Capacity_[MW]', value= single_deploy_2050['MW'], start_year=2000)


#trim to start in 2000, this trims module and materials
#had to specify and end year, cannot use to extend
sim3.trim_Years(startYear=2000, endYear=2150)


sim3.scenario['Perovskite'].dataIn_e.tail(4)#loc[49:55]


#PV ICE currently set up to read in a csv of bifi factors, so generate files to read in 
#need to adjust for length to 2150
idx_temp_2150 = pd.RangeIndex(start=2000,stop=2151,step=1) #create the index
df_temp_2150 = pd.DataFrame(index=idx_temp_2150, columns=['bifi'], dtype=float)
bifiPathDict_2150={}

for f in bifiFactors.keys(): #loop over module types
    bifi_2150 = df_temp_2150.copy() #copy of df
    bifi_2150['bifi'] = bifiFactors[f] #assign column
    bifipath_2150 = os.path.join(testfolder,'bifi_'+str(f)+'.csv') #create file path
    bifi_2150.to_csv(path_or_buf=bifipath_2150, index_label='year') #create file
    bifiPathDict_2150[str(f)] = bifipath_2150
    #append bifi path to dict? or list?


for scen in scennames:
    sim3.calculateFlows(scenarios=[scen], bifacialityfactors=bifiPathDict_2150[scen])


sim3_yearly, sim3_cumulative = sim3.aggregateResults()


allenergy3, energyGen3, energy_demands3 = sim3.aggregateEnergyResults()


allenergy3_cumu = allenergy3.cumsum()
energyGen3_cumu = energyGen3.cumsum()
energy_demands3_cumu = energy_demands3.cumsum()


energy_demands3_cumu_total = energy_demands3_cumu.filter(like='demand_total')
#energy_demands3_cumu_total


plt.bar(energy_demands3_cumu_total.columns, energy_demands3_cumu_total.loc[2150], 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Energy Demands Cumulative 2050 System')
plt.ylabel('Energy Demands [Wh]')


energyGen3_cumu.columns = energy_demands3_cumu_total.columns = scennames_labels
eroi_2050 = energyGen3_cumu.loc[2150]/energy_demands3_cumu_total.loc[2150]
eroi_2050


plt.bar(eroi_2050.index, eroi_2050, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('EROI of a 100 MW deployment in 2050')
plt.ylabel('Arbitrary units')


plt.plot(energyGen3/1e9)
plt.legend(scennames_labels)
plt.title('Energy Generation - Single 2050 System')
plt.ylabel('[GWh]')

	E_MFG_kWhpkg	E_mfgFuelFraction
1995	600.817735	0.064489
1996	527.042434	0.072326
1997	485.167804	0.077357
1998	432.693843	0.085473
1999	380.620553	0.095834
2000	269.697933	0.075408
2001	236.759317	0.066235
2002	204.221370	0.054188
2003	172.084094	0.037719
2004	159.669155	0.030097
2005	140.254886	0.022532
2006	130.586287	0.022463
2007	121.318358	0.022639
2008	112.451100	0.023120
2009	103.984511	0.023976
2010	95.918593	0.025298
2011	88.253345	0.027195
2012	88.388767	0.027307
2013	88.924859	0.027745
2014	89.861622	0.028498
2015	91.199054	0.029547
2016	76.337157	0.037576
2017	61.875930	0.049814
2018	62.915373	0.053028
2019	64.355487	0.056410
2020	74.096270	0.053503
2021	77.837724	0.055738
2022	77.837724	0.055738
2023	77.837724	0.055738
2024	77.837724	0.055738
2025	77.837724	0.055738
2026	77.837724	0.055738
2027	77.837724	0.055738
2028	77.837724	0.055738
2029	77.837724	0.055738
2030	77.837724	0.055738
2031	77.837724	0.055738
2032	77.837724	0.055738
2033	77.837724	0.055738
2034	77.837724	0.055738
2035	77.837724	0.055738
2036	77.837724	0.055738
2037	77.837724	0.055738
2038	77.837724	0.055738
2039	77.837724	0.055738
2040	77.837724	0.055738
2041	77.837724	0.055738
2042	77.837724	0.055738
2043	77.837724	0.055738
2044	77.837724	0.055738
2045	77.837724	0.055738
2046	77.837724	0.055738
2047	77.837724	0.055738
2048	77.837724	0.055738
2049	77.837724	0.055738
2050	77.837724	0.055738

	year	e_mat_extraction	e_mat_MFG	e_mat_MFG_fuelfraction	e_mat_MFGScrap_LQ	e_mat_MFGScrap_HQ	e_mat_Landfill	e_mat_EoL_ReMFG_clean	e_mat_Recycled_LQ	e_mat_Recycled_HQ	e_mat_Recycled_HQ_fuelfraction
25	2020	0.0062	169.06	0.22	24.5	600.0	0.09	0.4	1.6	138.380000	0.200000
26	2021	0.0062	170.73	0.21	24.5	600.0	0.09	0.4	1.6	143.040000	0.190000
27	2022	0.0062	169.05	0.20	24.5	600.0	0.09	0.4	1.6	77.837724	0.055738
28	2023	0.0062	168.80	0.20	24.5	600.0	0.09	0.4	1.6	77.837724	0.055738
29	2024	0.0062	169.09	0.20	24.5	600.0	0.09	0.4	1.6	77.837724	0.055738

	newInstalledCapacity_sim1_PV_ICE_[MW]	newInstalledCapacity_sim1_ex_PERC_50_[MW]	newInstalledCapacity_sim1_ex_High_eff_[MW]	newInstalledCapacity_sim1_ex_Perovskite_[MW]	newInstalledCapacity_sim1_ex_Lightweight_[MW]	newInstalledCapacity_sim1_r_PERC_[MW]	newInstalledCapacity_sim1_r_SHJ_[MW]	newInstalledCapacity_sim1_r_TOPCon_[MW]	newInstalledCapacity_sim1_r_IRENA_[MW]	newInstalledCapacity_sim1_r_RecycledPERC_[MW]	newInstalledCapacity_sim1_r_50PERC_[MW]	newInstalledCapacity_sim1_r_Perovskite_[MW]
year
2000	8.086690e+02	8.086690e+02	8.086690e+02	8.086690e+02	8.086690e+02	8.086690e+02	8.086690e+02	8.086690e+02	8.086690e+02	8.086690e+02	8.086690e+02	8.086690e+02
2001	1.099051e+03	1.099051e+03	1.099051e+03	1.099051e+03	1.099051e+03	1.099051e+03	1.099051e+03	1.099051e+03	1.099051e+03	1.099051e+03	1.099051e+03	1.099051e+03
2002	1.451322e+03	1.451322e+03	1.451322e+03	1.451322e+03	1.451322e+03	1.451322e+03	1.451322e+03	1.451322e+03	1.451322e+03	1.451322e+03	1.451322e+03	1.451322e+03
2003	1.990076e+03	1.990076e+03	1.990076e+03	1.990076e+03	1.990072e+03	1.990076e+03	1.990076e+03	1.990076e+03	1.990072e+03	1.990076e+03	1.990076e+03	1.990076e+03
2004	3.091790e+03	3.091790e+03	3.091790e+03	3.091790e+03	3.091766e+03	3.091790e+03	3.091790e+03	3.091790e+03	3.091766e+03	3.091790e+03	3.091790e+03	3.091790e+03
...	...	...	...	...	...	...	...	...	...	...	...	...
2096	1.266313e+08	9.261253e+07	1.769250e+08	2.923724e+08	1.719016e+08	1.256752e+08	1.256752e+08	1.256752e+08	1.813531e+08	1.347878e+08	9.735889e+07	1.836182e+08
2097	1.281286e+08	9.490018e+07	1.801410e+08	2.975159e+08	1.752298e+08	1.267427e+08	1.267427e+08	1.267427e+08	1.847511e+08	1.365921e+08	9.939645e+07	1.869677e+08
2098	1.297041e+08	9.726664e+07	1.833148e+08	3.027226e+08	1.785673e+08	1.281416e+08	1.281416e+08	1.281416e+08	1.881602e+08	1.384597e+08	1.014789e+08	1.901503e+08
2099	1.313593e+08	9.970472e+07	1.864349e+08	3.079838e+08	1.819004e+08	1.296230e+08	1.296230e+08	1.296230e+08	1.915703e+08	1.399069e+08	1.036012e+08	1.924769e+08
2100	1.331161e+08	1.022034e+08	1.894826e+08	3.132912e+08	1.851915e+08	1.307200e+08	1.307200e+08	1.307200e+08	1.949528e+08	1.418492e+08	1.057559e+08	1.957143e+08

Energy Analysis¶

Module Types Creation¶

Extreme modules¶

PERC_50¶

High Efficiency¶

Idealized Perovskite¶

Lightweighting¶

Evolving Realistic Modules¶

PERC¶

SHJ¶

TOPCon¶

IRENA Reg. Loss with improved recycling¶

Recycled PERC¶

50-year PERC gradual improvement¶

Hopeful Perovskite, Gradually increasing circularity¶

Modify Years 2000 to 2100¶

Apply deployment curve¶

Calculate Mass flow¶

Installation Compensation¶

Bifacial Factors¶

RESULTS: Effective Capacity and Replacements¶

RESULTS: Virgin Material Demands¶

RESULTS: Lifecycle Wastes¶

RESULTS: Energy¶

Energy Generation¶

Net Energy¶

System Level EROI¶

CE-TE Metric¶

EROI¶

EROI 2022¶

EROI of 2050 module¶

To Do¶

	year	new_Installed_Capacity_[MW]	mod_eff	mod_reliability_t50	mod_reliability_t90	mod_degradation	mod_lifetime	mod_MFG_eff	mod_Repair	mod_MerchantTail	...	mod_EOL_pg1_landfill	mod_EOL_pg2_stored	mod_EOL_pg3_reMFG	mod_EOL_pg4_recycled	mod_EOL_reMFG_yield	mod_EOL_sp_reMFG_recycle	mod_EOL_pb1_landfill	mod_EOL_pb2_stored	mod_EOL_pb3_reMFG	mod_EOL_pb4_recycled
0	2000	20.926667	13.420000	19.0	23.0	0.75	20.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0
1	2001	27.985000	13.600000	19.0	23.0	0.75	20.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0
2	2002	42.476000	13.722222	19.0	23.0	0.75	20.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0
3	2003	59.745000	13.844444	19.0	23.0	0.75	20.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0
4	2004	94.752000	13.966667	19.0	23.0	0.75	20.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
96	2096	0.000000	27.300000	25.0	30.0	0.80	25.0	98.0	0.0	0.0	...	0.0	0.0	100.0	0.0	98.0	100.0	0.0	0.0	100.0	0.0
97	2097	0.000000	27.300000	25.0	30.0	0.80	25.0	98.0	0.0	0.0	...	0.0	0.0	100.0	0.0	98.0	100.0	0.0	0.0	100.0	0.0
98	2098	0.000000	27.300000	25.0	30.0	0.80	25.0	98.0	0.0	0.0	...	0.0	0.0	100.0	0.0	98.0	100.0	0.0	0.0	100.0	0.0
99	2099	0.000000	27.300000	25.0	30.0	0.80	25.0	98.0	0.0	0.0	...	0.0	0.0	100.0	0.0	98.0	100.0	0.0	0.0	100.0	0.0
100	2100	0.000000	27.300000	25.0	30.0	0.80	25.0	98.0	0.0	0.0	...	0.0	0.0	100.0	0.0	98.0	100.0	0.0	0.0	100.0	0.0

	mod_MFG	mod_Install	mod_OandM	mod_Repair	mod_Demount	mod_Store	mod_Resell_Certify	mod_ReMFG_Disassembly	mod_Recycle_Crush	e_out_annual_[Wh]
0	5.133424e+10	5.251887e+07	0.0	0.0	0.000000e+00	0.0	0.0	0.0	0.000000e+00	1.312654e+12
1	1.838934e+10	1.860921e+07	0.0	0.0	0.000000e+00	0.0	0.0	0.0	1.419913e+00	1.774165e+12
2	2.235026e+10	2.237429e+07	0.0	0.0	0.000000e+00	0.0	0.0	0.0	1.097105e+02	2.332674e+12
3	3.424444e+10	3.391656e+07	0.0	0.0	0.000000e+00	0.0	0.0	0.0	1.342435e+03	3.189692e+12
4	7.015314e+10	6.875008e+07	0.0	0.0	0.000000e+00	0.0	0.0	0.0	7.714372e+03	4.954058e+12
...	...	...	...	...	...	...	...	...	...	...
96	1.609510e+13	4.513415e+10	0.0	0.0	3.374765e+10	0.0	0.0	0.0	2.068541e+12	9.753769e+16
97	1.311381e+13	3.677396e+10	0.0	0.0	2.354281e+10	0.0	0.0	0.0	1.557559e+12	9.789370e+16
98	1.718498e+13	4.819042e+10	0.0	0.0	3.619638e+10	0.0	0.0	0.0	2.248977e+12	9.824971e+16
99	1.819861e+13	5.103285e+10	0.0	0.0	3.940509e+10	0.0	0.0	0.0	2.419035e+12	9.860571e+16
100	1.347689e+13	3.779212e+10	0.0	0.0	2.337039e+10	0.0	0.0	0.0	1.611134e+12	9.896172e+16

	PV_ICE	50-year	High Eff	Idealized\nPerovskite\nSi-Tandem	Lightweight	PERC	SHJ	TOPCon	IRENA\nreg. loss	Recycled\nPERC	long-lived\nPERC	Hopeful\nPerovskite
year
2100	1.027132e+17	7.790149e+16	1.580354e+17	1.087181e+17	3.705405e+16	1.157625e+17	1.102412e+17	1.125710e+17	8.096957e+17	1.335163e+17	7.803863e+16	9.139532e+16

	PV_ICE	50-year	High Eff	Idealized\nPerovskite\nSi-Tandem	Lightweight	PERC	SHJ	TOPCon	IRENA\nreg. loss	Recycled\nPERC	long-lived\nPERC	Hopeful\nPerovskite
year
2100	4.860795e+18	5.233053e+18	5.252189e+18	4.854791e+18	4.926455e+18	5.195192e+18	5.299983e+18	5.248018e+18	4.153813e+18	5.127803e+18	5.232916e+18	4.872113e+18

	PV_ICE	50-year	High Eff	Idealized\nPerovskite\nSi-Tandem	Lightweight	PERC	SHJ	TOPCon	IRENA\nreg. loss	Recycled\nPERC	long-lived\nPERC	Hopeful\nPerovskite
year
2100	1.0	1.076584	1.08052	0.998765	1.013508	1.068795	1.090353	1.079662	0.854554	1.054931	1.076555	1.002328